Face Recognition in Video Using Deformable Parts Model with Scale Invariant Feature Transform (DPSIFT)

被引：1
|
作者：
Mohanraj, V. [1 ]
Vaidehi, V. [1 ]
Kumar, Ranajith [2 ]
Nakkeeran, R. [3 ]
机构：
[1] Anna Univ, Madras Inst Technol, Chennai, Tamil Nadu, India
[2] BARC, Mumbai, Maharashtra, India
[3] Christ Coll Engn & Technol, Pondicherry, India
来源：
ADVANCED COMPUTING AND SYSTEMS FOR SECURITY, VOL 2 | 2016年 / 396卷
关键词：
Face detection and recognition; SIFT; Deformable parts model; FLANN;
D O I：
10.1007/978-81-322-2653-6_5
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Face recognition is a complex task due to the challenges of varying pose, illumination, scaling, rotation, and occlusion in live video feed. This paper proposes a hybrid approach for face recognition in video called Deformable Parts Model with Scale Invariant Feature Transform (DPSIFT), to make face recognition system invariant to illumination, scaling, rotation, and limited pose. The proposed method identifies the significant points of the face using deformable part model and SIFT feature descriptors are extracted for those significant points. Fast Approximate Nearest Neighbor (FLANN) algorithm is used to match the SIFT descriptors between gallery image and probe image to recognize the face. The proposed method is tested with video datasets like YouTube celebrities, FJU, and MIT-India. DPSIFT method was found to perform better than the existing methods.
引用
收藏
页码：69 / 80
页数：12
相关论文
共 50 条
  • [41] Range image invariant face recognition using wavelet transform
    Pansang, S
    Attachoo, B
    Kimpan, C
    Phokharatkul, P
    7TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL X, PROCEEDINGS: SIGNALS PROCESSING AND OPTICAL SYSTEMS, TECHNOLOGIES AND APPLICATIONS, 2003, : 156 - 160
  • [42] Rotation Invariant Texture Recognition Using Discriminant Feature Transform
    Jundang, Nattapong
    Srisuk, Sanun
    ADVANCES IN VISUAL COMPUTING, ISVC 2012, PT II, 2012, 7432 : 440 - 447
  • [43] Collusion and rotation resilient video hashing based on scale invariant feature transform
    Neelima, A.
    Singh, K. M.
    IMAGING SCIENCE JOURNAL, 2017, 65 (01): : 62 - 74
  • [44] Analysis of Age Invariant Face Recognition Efficiency Using Face Feature Vectors
    Hast, Anders
    Zhou, Yijie
    Lai, Congting
    Blohm, Ivar
    ROBOTICS, COMPUTER VISION AND INTELLIGENT SYSTEMS, ROBOVIS 2024, 2024, 2077 : 47 - 65
  • [45] Fuzzy match index for scale-invariant feature transform (SIFT) features with application to face recognition with weak supervision
    Susan, Seba
    Jain, Abhishek
    Sharma, Aakash
    Verma, Shikhar
    Jain, Siddhant
    IET IMAGE PROCESSING, 2015, 9 (11) : 951 - 958
  • [46] Video Stabilization Using Principal Component Analysis and Scale Invariant Feature Transform in Particle Filter Framework
    Shen, Yao
    Guturu, Parthasarathy
    Damarla, Thyagaraju
    Buckles, Bill P.
    Namudari, Kameswara Rao
    IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2009, 55 (03) : 1714 - 1721
  • [47] FINGERPRINT CLASSIFICATION USING SCALE-INVARIANT FEATURE TRANSFORM
    Sobek, Jakub
    Cetnarowicz, Damian
    Dabrowski, Adam
    SPA 2011: SIGNAL PROCESSING ALGORITHMS, ARCHITECTURES, ARRANGEMENTS, AND APPLICATIONS CONFERENCE PROCEEDINGS, 2011, : 100 - 105
  • [48] Discrete Orthonormal Stockwell Transform based Feature Extraction for Pose Invariant Face Recognition
    Nithya, B.
    Sankari, Y. Bhavani
    Manikantan, K.
    Ramachandran, S.
    INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING TECHNOLOGIES AND APPLICATIONS (ICACTA), 2015, 45 : 290 - 299
  • [49] Localized discriminative scale invariant feature transform based facial expression recognition
    Soyel, Hamit
    Demirel, Hasan
    COMPUTERS & ELECTRICAL ENGINEERING, 2012, 38 (05) : 1299 - 1309
  • [50] Scale-Invariant Feature Transform Improvement for Problems of Recognition of Similar Images
    Kachalov, K. A.
    Kharchenko, S. S.
    PATTERN RECOGNITION AND IMAGE ANALYSIS, 2022, 32 (03) : 545 - 550
12345